Tell us about your role and the team/technology you handle at RevLifter.

Having worked with e-commerce retailers throughout my career, I have a solid understanding of their aims and challenges. RevLifter’s technology was designed to promote offers that used data to deliver incentives on a 1:1 level with customers. In today’s competitive landscape retailers need to be more selective and data-driven in how they use promotions, as margins are continually squeezed.

In my role as Chief Product and Strategy Officer the needs of our clients (the retailers) and the end-user (the customers) – and developing a product that caters to both – is a priority. It’s something I always keep in front of mind when developing new product features.

What runs the RevLifter data engine?

The data engine is built using Amazon Web Services (AWS). We leverage their support for streaming-data, petabyte-scale data-warehouses, and AI. AWS allows us to prototype quickly and scales easily as more customers are on-boarded.

Do you leverage AI ML algorithms at RevLifter? If yes, tell us more about your AI research and analytics.

We use AI in a way that can be tailored to each client but uses a common approach. In particular, we focus on goal-based AI where features can be carefully weighed depending on the retailer’s priorities. For example, if a mobile phone company wants to make the highest profit from selling handsets, we can help customers make a choice that is not only better for the retailer’s margins but a better deal for the customer. Over time data can help suggest better offers and predict the combinations of features in a package that will work best for both the consumer and retailer.

What is the current technology driving e-commerce Personalization?

Much of the data received through Analytics systems is unstructured and often supplemented with bespoke events for each individual site. Leveraging this torrent of data requires technologies that excel with schema-less and schema-lite data. Examples include Apache HBase and similar columnar data-stores.

Tell us about your latest funding and how you plan to extend the benefit to your customers?

Our latest funding will be used to develop our AI technology to analyze patterns in on-site behavior, in order to provide insights into the type of customers a retailer has and how likely they are to engage with an offer, if at all. In particular, we want to find ways to highlight incremental Sales, acquire new customers, and increase basket sizes and margins.

Eventually, we aim to develop an Omni-channel solution from on-site to in-store (O2O), to display retargeting and email. Gone are the days of classic email and display retargeting. No one appreciates being chased across the internet by that recently purchased pair of shoes.

Finally, our solution can be implemented in a matter of weeks. Traditional data companies often have long and expensive onboarding processes. Instead, our system can unlock the unstructured data on our clients’ websites very quickly, with little technical work needed from them.

What major developments have you been part of in the AI-based E-commerce market?

An area that we see huge potential growth in is the localization of content using AI. For example, we receive requests from fashion retailers to change the customer experience based on weather, UV or pollen patterns in North America. Local differences in what people buy are very different from region to region – utilizing AI offers a truly tailored approach based on specific customer types, geo-locations and even what stock is available to them.

How do you differentiate between various data points- Audience, Customer, Intent, Sentiment, and Big Data?

Rather than differentiate we prefer to take multiple perspectives on the data points, both individually and in aggregate. Most of these data types overlap with one another, so we take a holistic approach to our data processing.

How can marketers leverage E-commerce platforms with Intent data and digital signals to obtain true Identity Resolution?

Ideally, this would be done on a server-to-server basis rather than by relying on client-side JavaScript. The e-commerce platform can provide a unique customer identifier alongside the signals and marketers can be more confident in the accuracy of their data, as well as knowing that the originator has already taken into account the customer’s privacy settings. This would be a great improvement from the current landscape of JavaScript tags that attempt to derive intent and signals on the client-side – often running the risk of information leakage and falling foul of legislation such as GDPR.

Tell us about your Product roadmap for the next 2-3 years?

We are planning to move to an API-based service which will allow third parties to deliver personalized incentives quicker, while also connecting user identities across different Marketing channels. In the near future, we’re looking at creating solutions ranging from email to display, to driving footfall in-store.

We’re continually enhancing our Machine Learning capabilities and the more clients we work with, the more robust our system is becoming.

What are your predictions about your market/customer base in the next 3 years?

It will be less about who has the best deal and more about who is providing the best service and presenting the most relevant products and deals to the right customers at the right time. The key will be being able to do this at scale, which is what our technology is able to provide.

Ultimately, retailers will take back control and use automation more effectively to deliver very precise goals.